Analysis and Findings
Tourism and Seasonality in Portugal
The tourism industry worries a lot about seasonality, as it will affect the flow of visitors to tourist destinations. The hotel season is divided into two main seasons: high and low seasons. As the name suggests, high season is a busy season when the weather is good and the guests’ inflows are high; low season vice versa.
Portugal is no exception to this. The high season in Portugal usually runs in summer (June to September) and in spring (January to March); the beaches are usually the busiest in July and August. The low season generally occurs during the winter season, which begins around November and ends at the end of February. Weather during this time can display rainfall, unexpected rain and a strong, cold breeze which is not too ideal for sightseeing (lisbonlisboaportugal.com, n.d.).
Keeping the season in mind, we are interested in finding out any potential effect of seasonality on the guest count and the ADR of our hotels in the report.
Which months across the two year, saw the most inflow of the tourist ?
The inflow of visitors to these two hotels will help us decide the months in Portugal are the best time to travel to Portugal, and which hotel is the place to stay when you travel to Portugal.
Months are described in the order of occurrence (July was the first year of each year) to maintain the chronological order of the dataset. The overall pattern has been nearly the same for both hotels and years. The seasonality pattern was similar to the “W” shape, with the lower points of W occurring in the winter months from November to January, and the high points in the spring and summer periods. Interestingly, both hotels had the highest number of guests in the spring season in Year 1 (May and March) while in Year 2, the highest tourism was recorded in the summer season (August and October).
We can therefore infer that most of the months are a good time to visit Protugal, particularly from July to October and January to March, the graph above shows that most of the guests prefer to stay in City Hotel compared to the Resort Hotel.
Which segment of the hotel market is more profitable and lets customers book their trip easily ?
Study of which is the best way to book your ticket would allow customers to select their services when booking trips to the hotel
OTA has been the major player in the business segment of these hotels, it only took over the supremacy of group reservations in the city hotel in a half year period. In the first semester of 2016, the proportion of the OTA was doubled than in the previous semester.
The bookings through OTA have dominated since the beginning of the time observed, while at the resort hotel in comparison to the city hotel, the bookings via OTA marginally decreased in the first semester of 2016 and the group bookings increased or customers started booking on their own. We could also see that both hotels had hit the peak in the proportion of OTA bookings in semester 2 of 2016.
Aside from OTA matter, Figure 5.4 shows another interesting fact that in the condition of OTA booking was dominating the market segment, the proportion of direct booking in the resort hotel was relatively stable, this could be the reason may be cause this resort has their own way to promote their direct booking, for example may be through a loyalty voucher
Where Did The Bookings Come From ?
Another way to obtain more business information is to look at the roots of the travellers who book hotel space. An understanding of their actions and preference is essential. Therefore the hoteliers will establish strategies for attracting them.
We may examine which part of the world is most drawn to Portuguese
Figure 5.5 provides a booking map of the country of origin to get a view of the booking distribution, throughout the globe, The hover options, helps us with the count of the guests who have visited Portugal. If you hover over the map, it tell us that Portugal sees more tourists from Europe than the rest of the continents
As this could be a topic of interest for most of you and each individual, may want to know the count of guests that have visited Portugal. The interactive table below, allows you to get a detail view. The table suggests that the bookings came from 177 different countries.
Let’s see the effect of ADR on the two types of hotels with different types of guests.
Let’s try to find descriptive statistics on how the average daily rate impacts the various categories of guests in the hotel, respectively. Have the foreign guests been helping to increase the hotel profit. Lets us find out.
We have analysed that most bookings in both hotels are from international travelers and the ADR median presented in Figure 5.6 Shows that foreign guests in both types of hotels paid more money than the locals. Therefore we might argue that these travelers have become the hotels’ valued customers. The hover option, gives us more detail in the summary statistics and we can infer which 25 percentile/75 percentile values of ADR in Euros. This will be a good insight for us, when we have a certain limit on the expenditure for trips can keep this percentiles as reference and evaluate our desired expenditure
Since the international travellers have become the hotel’s valued guest, it is easier to retain them through personalization as a potential long-term customer. According to Criton (2019) personalization is the secret to the customer’s heart winning.
Let’s see, How do international guests like to reside in the hotels of Portugal ? How long do they they live there ?
With regard to the behavior that the guest could conduct, we would like to figure it out by looking at the time that the guest stayed in which hotels. Lets us find out which hotel was preferred for a longer stay.
Seems like there in the Portugal a lot to explore, from our analysis we note that most international guests want to stay for a longer period of time. We infer from figure 5.7 that, most tourist that travel to Portugal, like to stay for a longer period of time that is more than the weekend. Lets us try and understand, which hotel do the tourist like to stay in for their stay and does number of days of stay, give us insight on the the preference of the hotel.
Figure 5.8 helps us infer that if the stay is longer than the weekend and extends one or two day, than we see that the percent of bookings is greater in the city hotel. But on closer look, we understand that if the traveller stays for a longer time that is week and above, we might infer that, that the tourist would live in the resort hotel.
Lets us further find out if there any relationship, between the customer type and the ADR(Average Daily Rate) in the two different hotel types
During the analysis of this question, we will find out which customer types contribute the most towards which hotel type. Before we move towards our analysis we need to understand the type of these customers:
- Transient: Individuals or groups that occupy less than 10 rooms per night. These guests usually stay in the hotel short - term and require little services.
- Contract: bookings bound by contracts, usually for more than 30 days for a consistent block of rooms.
- Transient - Party: Transient booking but associated to other transient booking.
- Group: bookings associated to a group, usually occupy more than 10 rooms per night.
In the figure 5.9, we can infer that Individuals or groups that occupy less than 10 rooms per night can also be called as Transient customer, contribute more towards the City Hotel in terms of the ADR. Customers bound by contract, also contribute a slight more towards City hotel as compared to the resort hotel. But when we have bookings associated to a group, usually occupying more than 10 rooms per night, this type of customers will mostly choose to reside at the resort hotel and contribute towards it’s ADR. This finding also aligns with the previous research question about the length of stay. So we can infer that, when temporary customers or contract based customers visit Portugual, they will be mostly be residing at the City Hotel, thus contributing to the profits of the ADR in that particular hotel.
Whether there is any relationship between the the car parking space in the two different types of hotels mentioned in the dataset.
We will like to find out, if the car parking has some important role to play while the customers, and see how this varies, with the different variables
According to the figure 5.11 graph of the required number of car parking spaces against guest type, majority of the people whether the international or local do not need a car parking space, but there is a small percentage of people need one car parking space(4200 times belong to international guests, 3077 belong to local guest).
According to the figure 5.12 graph of the required number of car parking spaces against family type, majority of the people whether the family or not family do not need a car parking space, but there is a small percentage of people need one car parking space(1850 times belong to family, 6288 belong to not family).
According to the figure 5.13 graph of the required number of car parking spaces against hotel type, majority of the people whether the family or not family do not need a car parking space, but there is a small percentage of people need one car parking space(6402 times belong to transient, 133 belong to contract,797 belong to transient-party and 51 belong to group).
According to the figure figure 5.14 graph of the required number of car parking spaces against customer type, majority of the people whether the family or not family do not need a car parking space, but there is a small percentage of people need one car parking space(1921 times belong to city hotel, 5462 belong to resort hotel). It indicates that people who live in resort hotel prefer a car parking space, and i think that they may have a holiday here. There is an interesting thing, and resort hotel is asked twice by visitors with 8 car parking spaces. all the visitors are not family. it is pretty surprising since these people are transient-party rather than Group.
We mainly incorporated the function from tidyverse (Wickham et al. 2019), ggplot2 (Wickham et al. 2020), and plotly (Sievert et al. 2020). We also used lubridate (Spinu, Grolemund, and Wickham 2020), kableExtra (Zhu 2019), gridExtra (Auguie 2017), DT (Xie, Cheng, and Tan 2020), maps (Brownrigg 2018), viridis (Garnier 2018a), and viridisLite (Garnier 2018b) throughout the visualization.
Conclusion
In this study, we tried to explore the dataset in many angels. We compared the two hotels featured in this study across the different variables provided in the dataset.
We found out that the two hotels followed the overall seasonality trend in Portugal where high season falling in the spring and summer time. The ADR for two hotels were priced at a different rates with City hotel observed less fluctuation than Resort did. Also, by market segment, the ADR of the OTA and Direct booking channel appeared to be quite competitive even though the OTA’s prices were still a bit better. We explored that most of the customers book their trips for city hotel through travel agency whereas the resort hotel receive most direct or corporate bookings. We also did some research on which market segment is profiting the most semester wise and we saw that people have started booking their tickets more through online travel agency. We also saw the different places around the globe where Portugal received its tourist from and found that more European citizens, like to visit Portugal.
We further saw whether it was international guest that visited to these hotels or were these the local citizens, our analysis allowed us to infer that the percentage of international guest visitors was higher compared to local citizens. In addition, we concluded that how the different customers in these two different hotels contributed in ADR of these hotels and found out that only the group booking contributed most towards the Resort hotel, rest of them increased the ADR of the City Hotel. The meal type booked by different customers, helped us find out the information of the length of stay in these two hotels, furthermore we also analyzed about the parking information.
Overall, we really liked working on this project, it really makes us look forward to working on the exploratory data analysis on the different datasets.